Visible to the public Towards a data-driven behavioral approach to prediction of insider-threat

TitleTowards a data-driven behavioral approach to prediction of insider-threat
Publication TypeConference Paper
Year of Publication2018
AuthorsBasu, S., Chua, Y. H. Victoria, Lee, M. Wah, Lim, W. G., Maszczyk, T., Guo, Z., Dauwels, J.
Conference Name2018 IEEE International Conference on Big Data (Big Data)
ISBN Number978-1-5386-5035-6
KeywordsAtmospheric measurements, behavioral analysis, data-driven behavioral approach, Game-based Approach, Games, Human Behavior, industrial property, insider threat, Insider Threat Detection, insider threats, insider-threat related behavior, learning (artificial intelligence), Linguistics, Metrics, Organizations, Particle measurements, personality differences, personality variables, personality vulnerabilities, post-hoc personality analysis, psychology, pubcrawl, resilience, security of data, Task Analysis

Insider threats pose a challenge to all companies and organizations. Identification of culprit after an attack is often too late and result in detrimental consequences for the organization. Majority of past research on insider threat has focused on post-hoc personality analysis of known insider threats to identify personality vulnerabilities. It has been proposed that certain personality vulnerabilities place individuals to be at risk to perpetuating insider threats should the environment and opportunity arise. To that end, this study utilizes a game-based approach to simulate a scenario of intellectual property theft and investigate behavioral and personality differences of individuals who exhibit insider-threat related behavior. Features were extracted from games, text collected through implicit and explicit measures, simultaneous facial expression recordings, and personality variables (HEXACO, Dark Triad and Entitlement Attitudes) calculated from questionnaire. We applied ensemble machine learning algorithms and show that they produce an acceptable balance of precision and recall. Our results showcase the possibility of harnessing personality variables, facial expressions and linguistic features in the modeling and prediction of insider-threat.

Citation Keybasu_towards_2018